where \(w \in R^d\) , \(w_0 \in R\)  is our hyperplane, and \(x_i \in R^d, y_i\in \lbrace +1,-1\rbrace .\forall i=0...n-1\)  are our training data. Popular linear classifiers such as the linear support
vector machine, perceptron, and logistic regression [1]} can be considered
as convex approximations to this problem that yield fast gradient descent solutions [2]}.
However, they are also more sensitive to outliers than the 01 loss [2]}, [4]}, [5]}
and more prone to mislabeled data than 01 loss [6]}, [7]}, [8]}.
